General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. To ease the process, we have developed a framework that is capable of generating these ML-based forward solvers automatically. The framework uses an innovative training method that combines a predictive dimensionality reduction technique and a large data set of modeled GPR responses from our FDTD simulation software, gprMax. The forward solver is parameterized for a specific GPR application, but the framework can be extended in a straightforward manner to different electromagnetic problems.

翻译：通用全波电磁求解器,例如使用有限差异时间域(FDTD)方法的全波电磁求解器,在计算上要求模拟实际的GPR问题。我们探索以机器学习(ML)结构为基础的GPR近实时、前方模型方法的性能。为了缓解这一过程,我们开发了一个能够自动生成这些以ML为基础的远端求解器的框架。框架使用创新的培训方法,将预测性维度减少技术与我们FDTD模拟软件(gprMax)的大型模型GPR反应数据集结合起来。前方求解器为特定的GPR应用程序设定了参数,但框架可以直接扩大到不同的电磁问题。